@article{bacha_investigating_2025,

title = {Investigating food pairing hypothesis based on deep learning: Case of Algerian cuisine},

author = {S. Bacha and M. S. Allili and T. Kerbedj and R. Chahboub},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214793354&doi=10.1016%2fj.ijgfs.2024.101098&partnerID=40&md5=b2548861f182c4ea1820fceec7003f82},

doi = {10.1016/j.ijgfs.2024.101098},

issn = {1878450X (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {International Journal of Gastronomy and Food Science},

volume = {39},

abstract = {Traditional cuisine is considered a core element of cultural identity. The choice of food can often be influenced by identity, culture, and geography. This work investigates the traditional Algerian cuisine by exploring the food pairing hypothesis, which stipulates that combined ingredients with common flavor compounds taste better than their counterpart. To gain insight into the ingredients compounds found in this cuisine, we analyze their characteristics using spectral clustering. Then, we propose a model based on LSTMs to test the food pairing hypothesis in the Algerian cuisine on a collected corpus. Our research shows that the Algerian cuisine has a negative food pairing tendency, which is consistent with the South European cuisine, suggesting broader regional culinary patterns. To the best of our knowledge, this is the first study to investigate the FPH in Algerian cuisine, contributing to a deeper understanding of the food pairing tendencies specific to this region and offering a comparative perspective with neighboring Mediterranean cuisines. © 2025 Elsevier B.V.},

note = {Publisher: AZTI-Tecnalia},

keywords = {Algerian cuisine, Computational gastronomy, Deep learning, Food pairing hypothesis (FPH), Spectral clustering},

pubstate = {published},

tppubtype = {article}

}